Abstract
This work evaluates two artificial intelligence techniques for file distribution in Grid environments. These techniques are used to access data on independent servers in parallel, in order to improve the performance and maximize the throughput rate. In this work, genetic algorithms and Hopfield neural networks are the techniques used to solve the problem. Both techniques are evaluated for efficiency and performance. Experiments were conduced in environments composed of 32, 256 and 1024 distributed nodes. The results allow to confirm the decreasing in the file access time and that Hopfield neural network offered the best performance, being possible to be applied on Grid environments.
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de Mello, R.F., Andrade Filho, J.A., Dodonov, E., Porfirio Ishii, R., Yang, L.T. (2007). Optimizing Distributed Data Access in Grid Environments by Using Artificial Intelligence Techniques. In: Stojmenovic, I., Thulasiram, R.K., Yang, L.T., Jia, W., Guo, M., de Mello, R.F. (eds) Parallel and Distributed Processing and Applications. ISPA 2007. Lecture Notes in Computer Science, vol 4742. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74742-0_14
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DOI: https://doi.org/10.1007/978-3-540-74742-0_14
Publisher Name: Springer, Berlin, Heidelberg
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